knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(magrittr)
The goal of parsermd is to extract the content of an R Markdown file to allow for programmatic interactions with the document's contents (i.e. code chunks and markdown text). The goal is to capture the fundamental structure of the document and as such we do not attempt to parse every detail of the Rmd. Specifically, the yaml front matter, markdown text, and R code are read as text lines allowing them to be processed using other tools.
You can install the development version of parsermd from GitHub with:
This is a basic example which shows you the basic abstract syntax tree (AST) that results from parsing a simple Rmd file,
rmd = parsermd::parse_rmd(system.file("minimal.Rmd", package = "parsermd"))
The R Markdown document is parsed and stored in a flat, ordered list object containing tagged elements. By default the package will present a hierarchical view of the document where chunks and markdown text are nested within headings, which is shown by the default print method for
If you would prefer to see the underlying flat structure, this can be printed by setting
use_headings = FALSE with
print(rmd, use_headings = FALSE)
Additionally, to ease the manipulation of the AST the package supports the transformation of the object into a tidy tibble with
as.data.frame (both return a tibble).
and it is possible to convert from these data frames back into an
as_ast( as_tibble(rmd) )
Finally, we can also convert the
rmd_ast back into an R Markdown document via
cat( as_document(rmd), sep = "\n" )
Once we have parsed an R Markdown document, there are a variety of things that we can do with our new abstract syntax tree (ast). Below we will demonstrate some of the basic functionality within
parsermd to manipulate and edit these objects as well as check their properties.
rmd = parse_rmd(system.file("hw01-student.Rmd", package="parsermd")) rmd
Say we were interested in examining the solution a student entered for Exercise 1 - we can get access to this using the
rmd_select function and its selection helper functions, specifically the
rmd_select(rmd, by_section( c("Exercise 1", "Solution") ))
To view the content instead of the AST we can use the
rmd_select(rmd, by_section( c("Exercise 1", "Solution") )) %>% as_document()
Note that this gives us the Exercise 1 and Solution headings and the contained markdown text, if we only wanted the markdown text then we can refine our selector to only include nodes with the type
rmd_markdown via the
rmd_select(rmd, by_section(c("Exercise 1", "Solution")) & has_type("rmd_markdown")) %>% as_document()
This approach uses the tidyselect
& operator within the selection to find the intersection of the selectors
by_section(c("Exercise 1", "Solution")) and
has_type("rmd_markdown"). Alternative the same result can be achieved by chaining multiple
rmd_select(rmd, by_section(c("Exercise 1", "Solution"))) %>% rmd_select(has_type("rmd_markdown")) %>% as_document()
One useful feature of the
has_label() selection helpers is that they support glob style pattern matching. As such we can do the following to extract all of the solutions from our document:
rmd_select(rmd, by_section(c("Exercise *", "Solution")))
Similarly, if we wanted to just extract the chunks that involve plotting we can match for chunk labels with a "plot" prefix,
As mentioned earlier, the ast can also be represented as a tibble, in which case we construct several columns using the properties of the ast (sections, type, and chunk label).
tbl = as_tibble(rmd) tbl
All of the functions above also work with this tibble representation, and allow for the same manipulations of the underlying ast.
rmd_select(tbl, by_section(c("Exercise *", "Solution")))
As the complete ast is store directly in the
ast column, we can also manipulate this tibble using dplyr or similar packages and have these changes persist. For example we can use the
rmd_node_length function to return the number of lines in the various nodes of the ast and add a new length column to our tibble.
tbl_lines = tbl %>% dplyr::mutate(lines = rmd_node_length(ast)) tbl_lines
Now we can apply a
rmd_select to this updated tibble
rmd_select(tbl_lines, by_section(c("Exercise 2", "Solution")))
and see that our new
lines column is maintained.
Note that using the
rmd_select function is optional here and we can also accomplish the same task using
dplyr::filter or any similar approach
tbl_lines %>% dplyr::filter(sec_h3 == "Exercise 2", sec_h4 == "Solution")
As such, it is possible to mix and match between
parsermd's built-in functions and any of your other preferred data manipulation packages.
One small note of caution is that when converting back to an ast,
as_ast, or document,
as_document, only the structure of the
ast column matters so changes made to the section columns,
type column, or the
label column will not affect the output in any way. This is particularly important when headings are filtered out, as their columns may still appear in the tibble while they are no longer in the ast -
rmd_select attempts to avoid this by recalculating these specific columns as part of the subsetting process.
tbl %>% dplyr::filter(sec_h3 == "Exercise 2", sec_h4 == "Solution", type == "rmd_chunk")
tbl %>% dplyr::filter(sec_h3 == "Exercise 2", sec_h4 == "Solution", type == "rmd_chunk") %>% as_document() %>% cat(sep="\n")
tbl %>% rmd_select(by_section(c("Exercise 2", "Solution")) & has_type("rmd_chunk")) %>% as_document() %>% cat(sep="\n")
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.